Wide-band measurement and analysis techniques for the mobile radio channel

Cullen, P.J.; Fannin, P.C.; Molina, Ana María Anguiano

doi:10.1109/25.260751

Cited by 32 publications

(10 citation statements)

References 17 publications

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“…Bias: With and using (30), it easily follows that is unbiased, i.e., . Variance: We first consider the conditional variance of given the data (sounding) signal .…”

Section: Bias/variance Analysismentioning

confidence: 99%

See 1 more Smart Citation

Unbiased Scattering Function Estimators for Underspread Channels and Extension to Data-Driven Operation

Artes

Matz

Hlawatsch

2004

IEEE Trans. Signal Process.

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Abstract-We propose two methods for the estimation of scattering functions of random time-varying channels. In contrast to existing methods, our methods exploit the underspread property of these channels to achieve good estimation performance and low computational complexity. The first method uses a dedicated sounding to measure the channel. The second method uses the data signal of an ongoing data transmission as sounding signal and thus allows estimation without dedicated sounding. Both methods are effectively unbiased and can be implemented efficiently using the Zak transform. The performance of our scattering function estimators is studied both analytically by means of variance bounds and experimentally through numerical simulation, and their superiority over existing methods is demonstrated.

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“…Bias: With and using (30), it easily follows that is unbiased, i.e., . Variance: We first consider the conditional variance of given the data (sounding) signal .…”

Section: Bias/variance Analysismentioning

confidence: 99%

“…Inserting the above bounds for , , and in (40), we obtain the bound (27). APPENDIX C DERIVATION OF (30) We sketch the derivation of the "statistical input/output relation" (30). Our starting point is…”

Section: Appendix a Discretization Of Continuous-time Ltv Channelsmentioning

confidence: 99%

Unbiased Scattering Function Estimators for Underspread Channels and Extension to Data-Driven Operation

Artes

Matz

Hlawatsch

2004

IEEE Trans. Signal Process.

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“…Likewise, Figure 6b shows that the proposed (Con, In) model better fits certain in-building empirical data [12] than does the customary (Uni, In) model. The channel-sounding signal had a 250 MHz bandwidth in a seriously clustered environment.…”

Section: Li=1 Aii+lmentioning

confidence: 87%

A geometrical model for the toa distribution of uplink/downlink multipaths, assuming scatterers with a conical spatial density

Yu-hua

Wong

2008

IEEE Antennas Propag. Mag.

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The time-of-arrival (TOA) distributions of the uplink and downlink multipath are analytically derived in this paper. This is based on geometrical models that simplify the spatial relationship among a mobile transceiver, the scatterers, and a base-station transceiver. These models idealize the scatterers as lying on a circular disc centered around the mobile transceiver, with these scatterers concentrating in a conically shaped spatial density. The base-station transceiver may lie either among these scatterers (in an indoor propagation environment) or outside this disc of scatterers (for an elevated base-station outdoor receiver). In contrast to the customary uniform-disc density, this "conical" scatterer density indirectly accounts for the multipath scattering power loss. These new TOA distribution formulas, herein derived explicitly in terms of the model's only two independent parameters, can better fit some empirical data than can all earlier models that also confine all scatterers to within a circular disc.

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“…By adopting the Bayesian approach, the prior information p(x) can be used to derive a penalty term [9]. The Bayes' rule states that the optimal estimation of the samples is given by the argument that maximizes the pdf that is the most likely values of x provided that z has been observed (3) where is the likelihood function of the observations and is a scale factor.…”

Section: Enhancement Of Angle Of Arrival Estimation By Sparse Deconvomentioning

confidence: 99%

“…Normally, this can be improved and verified by channel sounding [1] [2]. The channel sounder is to find the equivalent low-pass time-varying impulse response for the channel, either directly or using pulse compression [3]. A limitation of this approach is that the response depends on both the physical channel and the radiation pattern of the antennas combinedchanging the antennas will alter the responses.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of angle of arrival estimation by sparse deconvolution algorithm

Yang

Hilton

Paul

2013

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Ray tracing is a technique often used to model indoor and outdoor environments. In order to improve the parameters used in ray tracing, measurements from channel sounding can be employed to test algorithms and build more accurate models of the channel. However, channel sounding can be limited as the antenna radiation pattern may change dramatically over a wide frequency span. As a consequence, discrepancies may arise between measurement by channel sounding and simulation by ray-tracing model. To overcome this limitation, in this paper sparse deconvolution will be adopted as a method to test and improve data measurements from real environments.Initial measurements are performed in a controlled (anechoic) environment where the sources of radiation can be specifically defined and show the effect of reduction in bandwidth on accuracy of the signal estimation. Comparisons are made between the Inverse Discrete Fourier Transform technique for processing the measured data and that using the Cauchy-Gaussian model. Good results are obtained for two different scenarios and it is shown that the Cauchy-Gaussian model requires less than half the bandwidth of the impulse response method using only IDFT.

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Wide-band measurement and analysis techniques for the mobile radio channel

Cited by 32 publications

References 17 publications

Unbiased Scattering Function Estimators for Underspread Channels and Extension to Data-Driven Operation

Unbiased Scattering Function Estimators for Underspread Channels and Extension to Data-Driven Operation

A geometrical model for the toa distribution of uplink/downlink multipaths, assuming scatterers with a conical spatial density

Enhancement of angle of arrival estimation by sparse deconvolution algorithm

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